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Activity Number:
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350
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Type:
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Invited
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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| Abstract - #303050 |
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Title:
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Bayesian Random Segmentation Models for Array-CGH Data
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Author(s):
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Veera Baladandayuthapani*+ and Jeffrey S. Morris and Yuan Ji
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Companies:
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The University of Texas M.D. Anderson Cancer Center and The University of Texas M.D. Anderson Cancer Center and The University of Texas M.D. Anderson Cancer Center
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Address:
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1515 Holcombe Blvd, Houston, TX, 77035,
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Keywords:
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CGh ; functional data analysis ; bayesian ; segmentation models ; random effects ; reversible jump
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Abstract:
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Array-based comparative genomic hybridization (array-CGH) provides a high-throughput, high-resolution method to measure relative changes in DNA copy number. These experiments typically yield data consisting of profiles of fluorescence intensity ratios of test and reference DNA samples across the whole chromosomal map. One of the goals of the analysis is characterization of these profiles into calling gains(amplifications) or losses(deletions) in copy numbers. These amplifications and deletions at the DNA level are important in the pathogenesis of cancer and other diseases. We present a Bayesian regression based approach to modeling these genomic profiles in the presence of multiple samples/arrays. The Bayesian model borrows strength across all arrays to call gains and losses at the population level as well as accounting for subject-specific deviations.
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